class_docstr: Base class for seq2seq models with embeddings
    
docstr: Returns the batch size of the curren batch based on the passed
  features.
docstr_tok: ["Returns","the","batch","size","of","the","curren","batch","based","on","the","passed","features","."]
code: def batch_size(self, features, labels):
      """Returns the batch size of the curren batch based on the passed
      features.
      """
      return tf.shape(features["source_ids"])[0]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import collections
  import tensorflow
  from seq2seq import graph_utils
  from seq2seq import losses
  from seq2seq.contrib.seq2seq.decoder import _transpose_batch_time
  from seq2seq.data import vocab
  from seq2seq.graph_utils import templatemethod
  from seq2seq.decoders.beam_search_decoder import BeamSearchDecoder
  from seq2seq.inference import beam_search
  from seq2seq.models.model_base import ModelBase, _flatten_dict
label_desc: Use image node_id 5 for calling the Seq2SeqModel obj's underlying member method code with example usage: obj.batch_size(features, labels) and returns: tf